Big Data, Big Demand: Navigating the Cloud Storage Landscape

Nicole Hemsoth

A rapid-fire search for the terms “big data” and “cloud storage” will reveal no shortage of options for users in need of a secure place to store and quickly access critical information. As the data deluge continues to slide a never-ending swell into already overstuffed datacenters, an increasing number of organizations are looking to the cloud to handle their massive demands—and for some, their processing needs as well.

A number of enterprise users are completely reliant on massive data wells to drive their businesses, and like high performance computing users, they have unique concerns when it comes to storage. However, given that the space is rather new for a an ever-diversifying breed of applications, storage concerns are overlooked in many conversations about big data.

To clear the clouds that muddle the big picture view of the storage landscape, we asked a number of big data storage experts about how they should evaluate cloud storage options and what the future of cloud storage looks like for both those with massive volumes of data to contend with as well as high performance computing users.

This week technology leaders from Panasas, EMC, NetApp, Cirtas, TwinStrata, Cleversafe, Virident, and Infineta Systems weighed in on a particular element involving cloud storage–from the nature of public clouds to scalability and cost concerns, to larger trends that are affecting the decision-making process.

Big Data and the Public Cloud Storage Problem

Garth Gibson, founder at CTO of Panasas provided a very directly-stated perspective on cloud storage, especially on public cloud storage. Gibson claims that the fundamental challenge with public cloud resources is that despite their focus on computation, the important element of storage is considered only as an afterthought.

“An opposing perspective is what is necessary for data-intensive HPC workloads. Here, big data is the true asset and computation is just part of the infrastructure. So instead of looking to potential big data applications to justify buying into the latest market hype on utility cloud computing in a far off state or country, the aggressive innovator focuses on raising top-line revenue and will spec the infrastructure (compute clusters) to the needs of the asset (big data). Some leasing company nearby, working with a colo facility and an integrator pouncing on all things posing as cloud software, will be more than happy to build and operate the private cloud appropriate for your Big Data. And at a reasonable price to boot.

Let’s get it straight. Understand your big data — what is it, where is it, what is needed to extract value out of it. And then build around it the private cloud best suited for it.”

Gibson remarked on the paradoxical problem with storing large amounts of data, stating that big data is “difficult and time-consuming to create, awkward to manage, expensive and slow to move, critical to gaining a competitive edge, and far from technically mature.”

This issue has been echoed from a number of quarters; the data is the central advantage for many businesses, yet it is also incredibly burdensome. Without solutions that are mature this creates a giant problem for many enterprise users. Furthermore, as Gibson asks, given the value of this data:

“Why would anyone shoehorn something that important into a public cloud—some arbitrary compute cluster at the other side of who knows where just to shave a few bucks off the apparent cost?” If high network bandwidth costs don’t kill the apparent advantage, the risks and weaknesses of privacy, efficiency, programmability, interactivity and vendor lock-in certainly do.”

While saving money is certainly a key to cloud computing decisions of any variety, Gibson’s point about the challenges for such cloud storage options are echoed by a number of other experts.

Adaptability and Cloud Storage

Along with the number of problems with cloud storage Gibson noted above, the issue of adaptability frequently emerges.

The process of choosing among vendors is already difficult given the multitude of different (yet highly similar in many ways) offerings and it can be difficult to determine how a chosen cloud storage solution is adaptable as needs change.

EMC recently acquired Isilon for its scale-out NAS offering for big data and file-based cloud opportunities. Director of Product Management for Isilon, Nick Kirsch, who was one of the engineers behind the first generation of their OneFS operating system weighed in on how high performance computing in particular can benefit from cloud storage and the scalability it provides. In Kirsch’s view,

“Given the often heterogeneous nature of HPC environments, maintaining flexibility and choice in your IT infrastructure is critical, especially in storage – where traditional systems often lock users into technology that will eventually require a fork-lift upgrade. While each form of cloud storage offers a solution to this problem, users need to make sure their chosen strategy can be adapted over time in an on-demand fashion as their needs change, which in an era of big data, they surely will.”

Whether users choose hosted cloud storage, an internal, private cloud, or a combination of both, the best practice is to ensure their chosen solution leverages on-demand storage resources that interoperate with server and networking technology – and vice versa – to enable greater flexibility and efficiency. To meet the needs of HPC and big data, storage must scale out without requiring more personnel and insulate users against unpredictable changes.”

Kirsch offered some advice for high performance computing users and those with big data demands, noting that it is indeed difficult to choose solutions that will be flexible. He suggests that in conversations with vendors about your cloud strategy, “keep them honest in offering products that can easily interoperate and scale in public, private or hybrid cloud environments. What is true today may not be true tomorrow, so freedom of choice is not only critical in any cloud storage strategy, but will, I believe, be a defining characteristic of success going forward.

New Generation of Applications Shaping the Cloud Storage Landscape

NetApp’s “Cloud Czar” Val Bercovici sees how a new generation of HPC and big data-driven applications are driving a more diverse cloud storage landscape, making scalability, flexibility and efficiency more important than ever. He notes that “Customers are increasingly facing a challenge in how to scale particularly in the backend with big data demands fueled by the mobility of data. To cope with these challenges, many customers are making the transition to flexible and efficient shared IT infrastructures—the foundation for cloud computing.

In Bercovici’s view, high-performance computing users actually have an excellent pedigree for Big Data applications since the storage layers for both share many common attributes. Big data platforms and applications predominantly depend on large sequential I/O transfers, not unlike rendering, seismic analysis or chip simulation. Consequently high bandwidth storage systems found in high-performance computing environments are well suited for big data applications.

Security and Cost Considerations of Cloud Storage

According to Cleversafe CEO, Chris Gladwin, the keywords for cloud storage are security and scalability. In Gladwin’s view, to receive the benefits of distributed collaboration, “cloud storage must be able to intrinsically secure the data even when it traverses the public internet.”

Gladwin notes that cloud storage has to be massively scalable—in the petabyte range and beyond but that there are considerations that users with big data must make that can influence cost. In his view, “cloud storage for big data must protect data without using replication since replication is simply too costly in petabyte scale. All this adds up to an architecture where data is securely virtualized and stored not as actual data, but instead stored on servers around the world.”

Trends on the Horizon for Big Data and Cloud Storage

Shirish Jamthe, Global Systems Director at Virident Systems sees changes in the entire landscape driving alterations in storage options as data growth increases. He notes that along with this mounting of data volumes comes a shift in terms of how applications interact with data, noting:

“In contrast to traditional data‐intensive applications such as database analytics, which have evolved over the years into disk‐friendly access patterns (medium‐to‐large sized blocks, sequential scans, seek penalty‐aware data structures, etc.), a new breed of data-intensive applications are emerging, which impose different, more challenging performance requirements on data storage devices. Examples of such applications include search, messaging, ad hoc data analytics, and social graph traversals – these applications tend to typically interact with unstructured data, performing a large number of small granularity accesses to random locations in the data collection. Such access patterns are markedly different from the carefully coordinated large-granularity accesses that characterize the traditional structured data applications.

Both of these trends–growth of data volumes, and the growing random access nature of applications outstripping advances in mechanical hard‐drive technologies–are exposing shortcomings in traditional disk drive‐based storage infrastructures, which are unable to keep up with the growing performance demands. Even currently, the most performance-intensive applications require fairly complex storage deployments, involving hundreds to thousands of disk spindles, and use of techniques such as multi-way striping and “short stroking” to meet the bandwidth and IOPS (I/O Operations Per Second) requirements, respectively. The cost, footprint, power requirements, and operational complexity of such deployments are fast making them untenable for the wide variety of situations that demand such performance levels.”

In Jamthe’s view the traditional SAN & NAS solutions have worked very well for companies for several years but have now started to show limitations against this explosive growth. The gap between DRAM capacity and disk storage has increased reducing the efficiency of DRAM cache. A new class of memory Storage-Class Memories (SCM) offers what he terms, “an interesting blending of the characteristics of both DRAM and disk. Storage built using these devices offer performance and random-access capabilities of DRAM, and the persistence and capacity of disk drives. NAND Flash-based Solid State Drive (SSD) is the first instance of SCM that is going main stream.” Of these solutions, Jamthe notes,

“A large number of companies, ranging from non-volatile memory manufacturers to server OEMs are offering NAND Flash-based SSDs in various form factors. All of these products are riding the Moore’s Law influenced scaling of NAND Flash capacities (with accompanying reductions in cost/GB), which is making possible disk drive‐like capacities for a moderate cost multiple. When one takes into consideration the hundred to thousand-fold improvements in random access performance possible with NAND Flash devices and their sharply dropping cost, it appears inevitable that SSDs will replace traditional disk drives, used as the performance storage tier, in majority of data-intensive applications in a few years.”

What’s Missing from Cloud Storage Conversations?

Haseeb Budhani, VP of Products at Infineta Systems notes that as organizations of all sizes (from research labs to enterprises) beginning to experiment with the likes of Hadoop, Cassandra, and other custom “distributed” or scatter-gather applications, the storage requirements to handle these large workloads are also growing steadily. He says that “storage growth is leading both enterprise, who have traditionally purchased from vendors such as EMC and IBM, to explore cheaper and more scalable storage alternatives. Enter cloud storage.”

In Budhani’s view the industry is focusing on the quickly-growing big data movement, “but the storage aspect of this movement doesn’t seem to have the industry’s full attention today. The storage problem is not simply I/O related (i.e. the speed at which increasingly fast computations need to access data), but also has to do with how much data is being stored, and where.” He goes on to offer the following opinon about what is missing from big data conversations:

With all this “big data” being generated and accessed at very high speeds, new concerns are quickly taking the spotlight. Some of these have to do with the fact that:

a) Access to this data from the customer’s primary business location needs to be reliable and fast;

b) When a customer’s data is “in the cloud,” it may technically reside across multiple locations; and

c) Depending on where the computation is taking place in the cloud, fast access to all of the customer’s data is critical.

The first issue above has been effectively addressed by the likes of Cirtas and StorSimple, and it remains to be seen whether businesses will continue to locally access data in the future. If all applications and requisite data reside in the cloud, there may not be any need for a cloud storage gateway.

The other two issues have been mostly left unaddressed to date. With data being housed across locations, it is essential that the WAN connectivity between these locations be extremely fast. Depending on the geographical location of a data center, it may not be trivial to provision additional bandwidth in short order. What’s more, in some cases, it is not the lack of bandwidth that is the concern, but the latency impact of using transport protocols such as TCP across sites that become a bottleneck.

The Final Word on Big Data and Cloud Storage

The jury is still out for high-performance computing users with applications that would suffer from cloud storage use but for those with massive data analytics challenges, there are benefits that can be realized in the arenas of scalability. One of the first questions that decision makers need to consider is what the tangible core benefit is to choosing cloud storage.

Josh Goldstein, who runs product management for Cirtas notes that there are relatively simple questions behind whether or not cloud storage for data-driven applications is a good route to pursue. Will the data be accessed frequently or does it just need to be kept? Secondly, if the data will be accessed, are the servers doing the processing in the cloud?

Goldstein suggests that “when data is infrequently accessed, leveraging public cloud storage via a cloud storage controller (known as a hybrid cloud model) offers attractive cost, data protection, and simplification benefits. Infrequent access is an important criteria here because moving “big data” back and forth to the cloud in short time periods is impractical.

He goes on to advise that when access to data is paramount, the decision boils down to where the processing will take place. “Once data is in the cloud, operating on it using cloud compute farms is very compelling, especially when the compute needs are elastic. If the intention is to use local compute resources, a private cloud storage model makes more sense as it avoids the large WAN transfers that might slow data processing.”

According to John Bates, co-founder and CTO at TwinStrata, Inc, there are clear benefits to using cloud versus physical storage for HPC users or those with big data problems to solve. Bates notes that for these challenges, “cloud storage can solve some of the problems associated with big data, particularly in the areas of resource planning and infrastructure growth costs. Cloud storage offers massive and automatic scalability, without requiring heavy capital expenditures on fixed storage systems that may reach capacity too fast.

Bates also feels that storing data in the cloud also enables more agility, leading to new service opportunities for big data analysis, such as in the field of business intelligence.

This concludes our survey of leaders from storage companies but we’d like to hear from you. If you’re considering the possibility of cloud storage for high-performance computing or big data needs, what are some of the essential questions that never get answered?

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